comparative analysis of statistical models in credit assessment by amos yaw ansah

166 Pages·2014·1.91 MB·English

by  Ansah Amos

University of Ghana http://ugspace.ug.edu.gh
COMPARATIVE ANALYSIS OF
STATISTICAL MODELS IN CREDIT
ASSESSMENT

BY
AMOS YAW ANSAH
(10357586)
A THESIS SUBMITTED TO THE SCHOOL
OF RESEARCH AND GRADUATE STUDIES
OF THE UNIVERSITY OF GHANA, LEGON,
IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE AWARD OF A
MASTER OF PHILOSOPHY (MPhil) IN
STATISTICS
JUNE, 2013
University of Ghana http://ugspace.ug.edu.gh
DECLARATION
I hereby declare that this thesis is the result of my own research work and that no part of
it has been presented for another degree in this university or elsewhere.

…………………………….

DATE ………………………….
Amos Yaw Ansah
(Candidate)

I hereby declare that the preparation and presentation of this thesis was supervised in
accordance with the guidelines on the supervision of thesis laid down by the University
of Ghana

……………………………………. …………………………………….
Dr. E. N. N. Nortey Dr. Isaac Baidoo
(PRINCIPAL SUPERVISOR) (CO- SUPERVISOR)
Date …………………………………. Date ………………………………

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ABSTRACT
With the emergence of the current financial crisis, important advances have been made in
credit risk management. Inherent in this management process is the assessment of
creditworthiness routine which subsequently leads to a credit granting decision. This
study is aimed at developing a statistical model that can be used to ascertain credit
assessment and to predict the probability of default of firms seeking credit from a
Ghanaian commercial bank. Subsequently, an attempt was made to find financial ratios
that can best be made used to successfully construct the model. To achieve these
purposes, the study employed the Probit and logit models for comparative reasons in
terms of their predictive abilities. Performance of the models was assessed using the
percentage correctly classified (PCC) and the area under the receiver operating
characteristics curved (AUC) where significant differences between the two models were
observed. It was found that both the Probit and the logit classifiers yield very good
performance rates but the logit model performed better for credit scoring. It was also
found that ratios bordering on assets to liability ratios, account receivable to liability,
Cash to Assets, current liability to total liabilities , Net current asset ,and total asset firm
size are those that were significantly helpful in scoring credit applicant. Practically the
model assist in reducing the time spent on evaluating credit applicant of each firm subject
to the model and also serve as a difference between application serving and portfolio
management . Indeed the multiplier effect will be a significant improvement in loan
portfolio quality of the model user.

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DEDICATION
To God Almighty ,in whom lies all the treasures of wisdom and knowledge .

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ACKNOWLEDGEMENT
To God be the Glory.
Indeed I am highly indebted to my project supervisors Dr. E.N .N Nortey, and Dr. Isaac
Baidoo whose suggestions, encouragements and unflinching support brought this work
this far.
My sincere thanks also go to all other lecturers in the Statistics Department, for their
patience, tolerance and assistance they offered me during my stay with them as a student.
I am also grateful to all the Non-teaching staff of the department especially Abass who
typed most portions of my work several times, without whose contribution this work
could not have been completed.
I am also indebted to Mr. Samuel Owusu, head of Mathematics Department, in
Akosombo International School and Rev. Simon Tinglafo , lecturer at Central University
College, for their immense and continuous encouragement and support
Not forgetting my friends Michael Larwer Tetteh and Felix A. Tetteh of Ghana
Commercial Bank and my siblings Ebenezer Rockson, Timothy, Enoch ,and Dinnah
Enyaah for their support and encouragement throughout the period of my study
May the Almighty God Bless Us All.

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TABLE OF CONTENTS
DECLARATION i
ABSTRACT ii
DEDICATION iii
ACKNOWLEDGEMENT iv
TABLE OF CONTENTS v
LIST OF TABLES xi
LIST OF FIGURES xii
LIST OF ABBREVIATIONS xiii
CHAPTER ONE: INTRODUCTION
1.0 Introduction 1
1.1 Background to the Study 1
1.2 Statement of the Problem 8
1.3 Objective of the study 10
1.4 Rationale of the Study 11
1.5 Scope and Limitation of the Study 11
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1.6 Organisation of the Study 12

CHAPTER TWO: LITTERATURE REVIEW
2.0 Introduction 13
2.1 Theoretical Literature 13
2.1.1 Concept of Credit Assessment 13
2.1.2 Concept of Credit Risk Management 16
2.1.3 Sources of Credit Risk 19
2.1.4 Internal Risk Factor of Credit Risk 20
2.1.5 Definition of Credit 21
2.1.6 The Credit Process 22
2.1.7 The Traditional Credit Process 24
2.1.8 The Modern Credit Process 25
2.1.9 Loan 27
2.1.10 Definition of Default 28
2.1.11 Theoretical Perspectives on the Performance of Probit and Logit Models 31
2.1.12 Performance Criteria of Prediction Model 32
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2.1.13 Similarities and Differences between Probit and Logit Models 36
2.2 Empirical Review 39
2.2.1 Financial Ratios as Predictors of Financial Default 39
2.2.2 Exploration of more Essential Ratios to Predict Default 41
2.2.3 Performance of financial distress/ Default Prediction Models 46
2.2.4 Comparing the Predictive Ability of Different Default Prediction Models 49
2.2.5 Further Review of Related Works 54

CHAPTER THREE: METHODOLOGY
3.0 Introduction 69
3.1 The Binary Dependent Variable 69
3.2 The Logit Model 70
3.2.1 Theoretical Logit Model 72
3.2.2 Assumptions of the Logit Regression 73
3.3 The Probit Model 74
3.3.1 Theoretical Probit Model 76
3.3.2 Assumption of the Probit Regression 77
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3.4 The Explanatory Variables 77
3.4.1 Financial Leverage Ratios 78
3.4.2 Liquidity Ratios 79
3.4.3 Profitability Ratios 80
3.4.4 Activity Ratio 81
3.4.5 Asset Turnover Ratio 82
3.4.6 Current Liabilities to Total Liabilities 82
3.4.7 Earning to interest expense 82
3.4.8 Net current asset 83
3.4.9 Total Liability to total Current ratio 83
3.4.10 Cash to Current Liabilities 83
3.4.11 Total asset 84
3.5 Data Source 84
3.6 Population of the Study 84
3.7 Sample Size 85
3.8 Assessing the Overall Model Fit 86
3.9 Model Prediction Performance Assessment 86
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3.10 Specification Error Test 88
3.11 Data Analysis 88
CHAPTER FOUR: DATA ANALYSIS
4.0 Introduction 90
4.1 Description Statistics 90
4.2 Correlation Analysis and Variance Inflation Tests 95
4.3 Analysis of Logit Results 99
4.3.1 Interpretation of Estimated Parameters 99
4.3.2 Interpretation of the Logit Model Statistics 101
4.3.3 Classification Accuracy of the Logit Model 102
4.4 Analysis of the Probit Results 105
4.4.1 Interpretation of Estimated Parameter 105
4.4.2 Interpretation of Probit Model Statistics 106
4.4.3 Classification Accuracy of the Probit Model 107
4.5 Validation Results 109

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